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| Item Type: | Review |
|---|---|
| Title: | Toward identification of functional sequences and variants in noncoding DNA |
| Creators Name: | Monti, R. and Ohler, U. |
| Abstract: | Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power. |
| Keywords: | Gene Regulation, Enhancer, Transcription, Sequence Analysis, Machine Learning, Deep Learning, Rare Variants, Whole Genome Sequencing, Genome Wide Association Studies, Variant Effect Prediction |
| Source: | Annual Review of Biomedical Data Science |
| ISSN: | 2574-3414 |
| Publisher: | Annual Reviews |
| Volume: | 6 |
| Page Range: | 191-210 |
| Date: | August 2023 |
| Official Publication: | https://doi.org/10.1146/annurev-biodatasci-122120-110102 |
| PubMed: | View item in PubMed |
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